Generating feedback for a target content item based on published content items

ABSTRACT

Techniques for generating feedback for an unpublished content item based on published content items are disclosed. A content feedback engine identifies published content items associated with similar attributes as the unpublished content item. Effectiveness scores of the published content items are determined. The content feedback engine determines an effectiveness score for a portion of the unpublished content item based on the effectiveness scores of the published content items. The content feedback engine presents a graphical indication that marks the portion of the unpublished content item based on the effectiveness score for the portion of the unpublished content item. Additionally or alternatively, the content feedback engine recommends content to be added to and/or removed from the unpublished content item based on the content and/or attributes of the published content items.

TECHNICAL FIELD

The present disclosure relates to content items. In particular, thepresent disclosure relates to generating feedback for a target contentitem based on one or more published content items.

BACKGROUND

A content item may be published to a public forum, such as a socialmedia platform, a website, a mobile application, a web application,and/or an online forum. Prior to publishing a content item, an author ofthe content item may modify the content item multiple times. After thecontent item is published, a particular number of users may view thecontent item. Users who have viewed the content item may also react tothe content item, such as posting a comment regarding the content item,sharing the content item, following a hyperlink included in the contentitem, and/or purchasing a product advertised by the content item.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings. It should benoted that references to “an” or “one” embodiment in this disclosure arenot necessarily to the same embodiment, and they mean at least one. Inthe drawings:

FIG. 1 illustrates a content feedback system, in accordance with one ormore embodiments;

FIG. 2 illustrates an example set of operations for determining aneffectiveness score for at least a portion of a target content item, inaccordance with one or more embodiments;

FIG. 3 illustrates an example set of operations for recommending contentto be added to and/or removed from a target content item, in accordancewith one or more embodiments;

FIG. 4 illustrates an example user interface for presenting a predictedeffectiveness score and recommended content for a target content item,in accordance with one or more embodiments;

FIG. 5 illustrates an example user interface for presenting graphicalindications that mark portions of a target content item based oneffectiveness scores for the portions of the target content item, inaccordance with one or more embodiments; and

FIG. 6 shows a block diagram that illustrates a computer system inaccordance with one or more embodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding. One or more embodiments may be practiced without thesespecific details. Features described in one embodiment may be combinedwith features described in a different embodiment. In some examples,well-known structures and devices are described with reference to ablock diagram form in order to avoid unnecessarily obscuring the presentinvention.

-   -   1. GENERAL OVERVIEW    -   2. CONTENT FEEDBACK SYSTEM ARCHITECTURE    -   3. DETERMINING AN EFFECTIVENESS SCORE FOR AT LEAST A PORTION OF        A TARGET CONTENT ITEM    -   4. RECOMMENDING CONTENT TO BE ADDED TO AND/OR REMOVED FROM A        TARGET CONTENT ITEM    -   5. EXAMPLE EMBODIMENTS    -   6. MISCELLANEOUS; EXTENSIONS    -   7. HARDWARE OVERVIEW

1. General Overview

One or more embodiments include determining an effectiveness score for aportion of a target content item that is being drafted. A contentfeedback engine identifies published content items with similarattributes as at least the portion of the target content item. Thecontent feedback engine determines an effectiveness score of eachpublished content item. The content feedback engine may determine aneffectiveness score of a published content item based on, for example, alevel of user engagement and/or user sentiment associated with thepublished content item. The content feedback engine determines aneffectiveness score for the portion of the target content item based onthe effectiveness scores of the published content items. The contentfeedback engine presents, on a graphical user interface, a graphicalindication that marks the portion of the target content item based onthe effectiveness score for the portion of the target content item. Thecontent feedback engine may determine effectiveness scores for multipleportions of the target content item. The content feedback engine maypresent different graphical indications marking different portions ofthe target content item based on the different effectiveness scoresassociated with each portion. The content feedback engine may determinethe effectiveness scores for one or more portions of the target contentitem while the target content item is being drafted.

One or more embodiments include recommending content to be added toand/or removed from a target content item. A content feedback engineidentifies published content items with similar attributes as the targetcontent item. The content feedback engine determines an effectivenessscore of each published content item. The content feedback engineselects content from the published content items based on the respectiveeffectiveness scores. The content feedback engine generates arecommendation for adding and/or removing the selected content to thetarget content item. Additionally or alternatively, the content feedbackengine generates a recommendation for replacing current content in thetarget content item with content that is selected from the publishedcontent items. The content feedback engine may generate a recommendationfor the target content item while the target content item is beingdrafted.

One or more embodiments described in this Specification and/or recitedin the claims may not be included in this General Overview section.

2. Content Feedback System Architecture

FIG. 1 illustrates a content feedback system, in accordance with one ormore embodiments. As illustrated in FIG. 1, a content feedback system100 includes a user interface 110, a target content item 112, a contentfeedback engine 102, a data repository 108, an effectiveness score 122for a portion of the target content item, and recommended content 124.In one or more embodiments, the system 100 may include more or fewercomponents than the components illustrated in FIG. 1. The componentsillustrated in FIG. 1 may be local to or remote from each other. Thecomponents illustrated in FIG. 1 may be implemented in software and/orhardware. Each component may be distributed over multiple applicationsand/or machines. Multiple components may be combined into oneapplication and/or machine. Operations described with respect to onecomponent may instead be performed by another component.

In one or more embodiments, a target content item 112 is a content itemfor which feedback is being generated by a content feedback engine 102.The target content item 112 may be an uncompleted content item that isbeing drafted by a user. Additionally or alternatively, the targetcontent item 112 may be a content item that has been published and isbeing modified by a user.

A target content item 112 may be published as a social post accessibleto other users on a social networking platform. A social networkingplatform is any platform that allows users to communicate with eachother. In an embodiment, a social networking platform is an onlineplatform that is used by people to build social networks and/or socialrelations with other people who share similar personal or careerinterests, activities, backgrounds or real-life connections. Examples ofsocial networking platforms include postings platforms, news platforms,discussion platforms, forums, webpages, and/or image or video sharingplatforms.

A target content item 112 is associated with one or more attributes 114.An attribute 114 may be a component or portion of a target content item112. Components of a target content item include, for example, text,images, references, and social handles. A reference is another contentitem that is mentioned in the target content item 112. The reference maybe identified by a hyperlink or other identifier. A social handle is anidentifier of a user and/or a user account of a social media platform.

An attribute may be associated with a particular component or portion ofa target content item 112. Examples of attributes 114 include topics,labels, hashtags, and indicators associated with text and/or imagesincluded in a target content item 112. A topic is a general themeassociated with the text and/or image. A label is a specific product,service, person, and/or item associated with the text and/or image. Anindicator is an attitude and/or emotion conveyed by the text and/orimage. As an example, a target content item may state, “Company A haslaunched an exciting new line of cars: Model XYZ.” A topic associatedwith the text may be “Car.” A label associated with the text may be“Model XYZ.” An indicator associated with the text may be “Positive” or“Excitement.” The topic, label, and indicator may be stored asattributes associated with the target content item.

Additionally or alternatively, examples of attributes 114 includegeotags, locations, and timestamps associated with an image included ina target content item 112. A geotag is a tag indicating a geographicallocation at which the image was generated. A location is an environmentand/or setting in which the image was generated. A timestamp indicates atime at which the image was generated. As an example, a target contentitem may include a photo of three people in Restaurant B. A geotagassociated with the photo may indicate that the photo was captured at“37.58° N, 122.35° W.” A location associated with the photo may be“Indoors” or “Restaurant.”

Additionally or alternatively, examples of attributes 114 include anumber of followers of a social handle included in a target content item112. Further, examples of attributes 114 include topics associated witha social handle included in a target content item 112. As an example, atarget content item may include the social handle, “@MaryAnn.”“@MaryAnn” may be a social media account that has 10,000 followers.Further, “@MaryAnn” may be a social media account that includes postingsabout the software industry. An attribute of the social handle may be anumber of followers of the “@MaryAnn” social media account, which is10,000 is this example. Further, an attribute of the social handle maybe a topic associated with the social handle, which is “SoftwareIndustry” in this example.

Additionally or alternatively, examples of attributes 114 include anumber of viewers, a number of likes, and a number of commentsassociated with a reference included in a target content item 112.Further, examples of attributes 114 include an effectiveness score (suchas effectiveness scores 120 a-b) associated with a reference included ina target content item. Effectiveness scores 120 a-b are furtherdescribed below.

An attribute 114 may be related to how a target content item 112 isgenerated. An attribute 114 may be, for example, an author of a targetcontent item 112, a time at which a target content item 112 was created,an application and/or device that is used to generate a target contentitem 112.

An attribute 114 may be a characteristic of a target audience of atarget content item 112. Examples of characteristics of a targetaudience may include age, gender, demographics, geographical location,interests, and/or activity history. As an example, a target audience ofa target content item may be women who have searched for baby productsin the past thirty days. As another example, a target audience of atarget content item may be persons located within the state ofCalifornia.

In one or more embodiments, a user interface 110 refers to hardwareand/or software configured to receive user input for generating,drafting, modifying, and/or publishing a target content item 112. Theuser interface 110 is configured to present graphical indications thatmark portions of the target content item 112 based on effectivenessscores associated with the respective portions of the target contentitem 112. A user interface 110 renders user interface elements andreceives user input via user interface elements. Examples of userinterfaces 110 include a graphical user interface (GUI), a command lineinterface (CLI), a haptic interface, and a voice command interface.Examples of user interface elements include checkboxes, radio buttons,dropdown lists, list boxes, buttons, toggles, text fields, date and timeselectors, command lines, sliders, and pages.

In one or more embodiments, a data repository 108 is any type of storageunit and/or device (e.g., a file system, database, collection of tables,or any other storage mechanism) for storing data. Further, a datarepository 108 may include multiple different storage units and/ordevices. The multiple different storage units and/or devices may or maynot be of the same type or located at the same physical site. Further, adata repository 108 may be implemented or may execute on the samecomputing system as a content feedback engine 102. Alternatively oradditionally, a data repository 108 may be implemented or executed on acomputing system separate from a content feedback engine 102. A datarepository 108 may be communicatively coupled to a content feedbackengine 102 via a direct connection or via a network.

Information describing published content items 116 a-b may beimplemented across any of components within the system 100. However,this information is illustrated within the data repository 108 forpurposes of clarity and explanation.

In one or more embodiments, a published content item (such as publishedcontent items 116 a-b) is a content item that has been published. Acontent item may be published to a public forum, such as a social mediaplatform, a website, a mobile application, a web application, and/or anonline forum. A published content item may be a content item that waspreviously published by the same user that is drafting a target contentitem 112. Alternatively, a published content item may be a content itemthat was previously published by another user.

A published content item is associated with one or more attributes (suchas attributes 118 a-b). Examples of attributes are described above withreference to the attributes 114 associated with a target content item112.

In addition to the examples described above, an attribute of a publishedcontent item may be a characteristic of a detected audience of thepublished content item. Characteristics of a detected audience of apublished content item may be determined via monitoring cookies, userlogins, and/or activities of users as users navigate through variouswebsites and/or applications. Examples of characteristics of a detectedaudience may include age, gender, demographics, geographical location,interests, and/or activity history.

A published content item is associated with an effectiveness score (suchas effectiveness scores 120 a-b). An effectiveness score is based on anengagement score and/or a sentiment score, as described below.

In an embodiment, an effectiveness score is based on an engagementscore. An engagement score is a measure of a level of engagementassociated with a published content item. The level of engagement may bebased on, for example, a number of viewers of the content item, a numberof likes associated with the content item, a number of commentsassociated with the content item, a number of users who shared thecontent item, and/or a number of users who took an action in associationwith the content item. An action associated with the content item mayinclude, for example, clicking on a hyperlink included in the contentitem, and/or purchasing a product described by the content item.

In an embodiment, an effectiveness score is based on a sentiment score.A sentiment score is a measure of a level of sentiment associated with apublished content item. Sentiment refers to an attitude, emotion, and/oropinion of viewers of the published content item. The level of sentimentis determined by monitoring a user response and/or reaction to apublished content item. Based on the user response and/or reaction, asentiment score associated with the published content item may indicatea positive attitude or a negative attitude towards the published contentitem.

The level of sentiment may be determined based on, for example, whetherthe comments on a published content item are positive or negative,and/or whether the published content item is being shared in a positiveor negative manner. Additionally or alternatively, the level ofsentiment may be determined based on a user's discussion with otherssubsequent to the user viewing the published content item. The comments,shares, and/or discussions associated with the content item may includetext, images, emoticons, and/or other content. As an example, apublished content item may state, “Chicken nuggets for only $10.99.” Auser response may include a sad face emoticon. A comment to thepublished content item may state, “I would never buy chicken nuggets atthat price.” Another comment may state, “That is not a deal at all.”Based on the comments, a sentiment score associated with the publishedcontent item may indicate a negative attitude towards the publishedcontent item. As another example, a user may transmit a message thatincludes a reference to a published content item to a friend. Themessage may include a smile emoticon and a suggestion for the friend toread the published content item. Alternatively, the message may includean angry emoticon and state “I can't believe how ridiculous this is.”Based on the messages, a content feedback engine may determine asentiment score associated with the published content item.

The level of sentiment may be determined based on, for example, aphysical reaction of a user. As an example, a content feedback enginemay be configured to translate user behavior captured by a webcam touser sentiment. As an example, video captured by a webcam may beanalyzed to determine that a user was frowning while reading publishedcontent. Based on the frown, a content feedback engine may determine asentiment score of 2 out of 5. User behavior which may be translated toa sentiment score includes, but is not limited to, user posture (e.g.,slouching or leaning forward) and facial expressions (e.g., smiling,screaming, grimacing, laughing, yelling, etc.).

In an embodiment, an effectiveness score is a combination of anengagement score and a sentiment score. As an example, an effectivenessscore may an average of an engagement score and a sentiment score. Asanother example, an effectiveness score may be a weighted combination ofthe engagement score and the sentiment score. The weight assigned toeach of the engagement score and the sentiment score may depend, forexample, on how much relevant data was gathered for the determining theengagement score and the sentiment score, respectively.

In one or more embodiments, a content feedback engine 102 includes ascore determination module 104 and a content recommendation module 106.In an embodiment, a content feedback engine 102 may include one modulewithout including the other module. In an embodiment, the two modulesmay be combined as one module or component.

In an embodiment, a score determination module 104 refers to hardwareand/or software configured to perform operations described herein fordetermining an effectiveness score 122 for at least a portion of atarget content item 112. The effectiveness score 122 for at least theportion of the target content item 112 may be determined based onmultiple effectiveness scores corresponding to multiple publishedcontent items. Examples of operations for determining an effectivenessscore for at least a portion of a target content item are describedbelow with reference to FIG. 2. As illustrated, the effectiveness score122 for a portion of a target content item 112 predicts how effective ahypothetical content item, having the portion of the target contentitem, would be as a social post accessible to other users on at leastone social networking platform. The hypothetical content item may be,for example, the target content item (including the portion), theportion of the target content item posted by itself, and/or content frompublished content items having the first portion.

In an embodiment, a content recommendation module 106 refers to hardwareand/or software configured to perform operations described herein forrecommending content 124 to be added to and/or removed from a targetcontent item 112. Examples of operations for recommending content aredescribed below with reference to FIG. 3. As illustrated, recommendedcontent 124 is content that is recommended for being added to a targetcontent item 112.

In an embodiment, a content feedback engine 102 is implemented on one ormore digital devices. The term “digital device” generally refers to anyhardware device that includes a processor. A digital device may refer toa physical device executing an application or a virtual machine.Examples of digital devices include a computer, a tablet, a laptop, adesktop, a netbook, a server, a web server, a network policy server, aproxy server, a generic machine, a function-specific hardware device, amainframe, a television, a content receiver, a set-top box, a printer, amobile handset, a smartphone, a personal digital assistant (“PDA”).

3. Determining an Effectiveness Score for at Least a Portion of a TargetContent Item

FIG. 2 illustrates an example set of operations for determining aneffectiveness score for at least a portion of a target content item, inaccordance with one or more embodiments. One or more operationsillustrated in FIG. 2 may be modified, rearranged, or omitted alltogether. Accordingly, the particular sequence of operations illustratedin FIG. 2 should not be construed as limiting the scope of one or moreembodiments. The operations illustrated in FIG. 2 may be performed by acontent feedback engine 102, a score determination module 104, anothercomponent or module, and/or a combination thereof.

One or more embodiments include determining attributes of at least aportion of a target content item (Operation 202). A content feedbackengine 102 (and/or a score determination module 104 thereof) obtains thetarget content item. The target content item is received from a userinterface. The target content item may be an incomplete piece ofcontent. As an example, the target content item may include only half ofa paragraph. As another example, the target content item may include anincomplete sentence. The content feedback engine 102 may obtain acurrent version of the target content item, while a user continues todraft and/or modify the target content item.

The content feedback engine 102 identifies one or more of the followingcomponents of the target content item: text, images, references, andsocial handles. Additional and/or alternative components of the targetcontent item may be identified.

In an embodiment, the content feedback engine 102 analyzes the text todetermine topics, labels, indicators, and/or other attributes associatedwith the text. The content feedback engine 102 may determine attributesassociated with the text by performing natural language processingand/or semantic analysis. The content feedback engine 102 may furtheranalyze the text based on a library and/or table of existing topics,labels, and indicators. The content feedback engine 102 may find thattopics, labels, and indicators in the library appear within the text.The content feedback engine 102 determines that the topics, labels, andindicators found within the text are attributes associated with thetext.

As an example, a target content item may state, “The Model XYZsmartphone is loaded with the best camera and the best speakers.” Acontent feedback engine may parse the text to determine the grammaticalcomponents of the text. Based on the parsing, the content feedbackengine 102 may identify “Model XYZ smartphone” as the subject of thesentence. The content feedback engine 102 may compare “Model XYZsmartphone” to a library of existing topics and labels. The library mayinclude the topic “Smartphone.” The library may include the label “ModelXYZ.” The library may also indicate that the label “Model XYZ” isassociated with another label “Company A,” which is the company thatproduces Model XYZ smartphone. Based on the library, the contentfeedback engine may determine that the text is associated with the topic“Smartphone,” the label “Model XYZ,” and the label “Company A.”

In an embodiment, the content feedback engine 102 analyzes an image inthe target content item to determine topics, labels, indicators, and/orother attributes associated with the image. The content feedback engine102 analyzes the image to identify objects, places, and/or persons inthe image. The content feedback engine 102 compares the image to alibrary of known objects, places, and/or persons. Each of the knownobjects, places, and/or persons are tagged with topics, labels, and/orindicators. The content feedback engine determines a match between theimage in the target content item and a particular image in the library.The content feedback engine 102 determines that the topics, labels,and/or indicators associated with the particular image in the libraryare attributes associated with the image in the target content item.

As an example, an image may show a face of a person. A content feedbackengine 102 may compare the image to a library of known faces. Based onthe comparison, the content feedback engine 102 may identify the face inthe image as that of Mary Smith. The content feedback engine 102 maydetermine that a label associated with the image is “Mary Smith.”Additionally, the content feedback engine 102 may determine that theface in the image is smiling. The content feedback engine 102 maydetermine that an indicator associated with the image is “Positive.”

Additionally or alternatively, the content feedback engine 102 analyzesthe image to determine a geotag, a location, and a timestamp associatedwith the image. The content feedback engine 102 obtains a geotag and/ora timestamp associated with the image. Further, the content feedbackengine 102 analyzes the image to determine a setting and/or environmentdepicted in the image. As an example, an image may show an outdoor sceneunder bright sunlight. Based on an analysis of the colors included inthe image, a content feedback engine 102 may determine that the image isbright. The content feedback engine 102 may determine that the imagedepicts a bright outdoor setting.

In an embodiment, the content feedback engine 102 analyzes a socialhandle included in the target content item to determine attributesassociated with the social handle. The content feedback engine 102identifies a social media platform associated with the social handle.The content feedback engine 102 determines a number of followers of thesocial handle from the social media platform. The number of followers isan attribute associated with the social handle. Further, the contentfeedback engine 102 identifies postings generated using the socialhandle from the social media platform. The content feedback engine 102analyzes the postings to determine topics, labels, and indicatorsassociated with the postings. The content feedback engine 102 identifiesthe topics, labels, and indicators as attributes associated with thesocial handle.

In an embodiment, the content feedback engine 102 analyzes a referenceincluded in the target content item to determine attributes associatedwith the reference. The reference may be identified by hyperlink, a filelocation, and/or other identifier. The content feedback engine 102retrieves the reference from a website and/or server. The contentfeedback engine 102 determines a number of viewers, a number of likes,and a number of comments associated with the reference. The contentfeedback engine 102 identifies the number of viewers, the number oflikes, and the number of comments as attributes associated with thereference.

One or more embodiments include identifying published content itemsassociated with similar attributes as at least the portion of the targetcontent item (Operation 204). The content feedback engine 102 analyzespublished content items stored in one or more data repositories. Thecontent feedback engine 102 determines attributes associated with eachpublished content item. Examples for determining attributes associatedwith a content item are described above with reference to Operation 202.

The content feedback engine identifies a subset of the published contentitems that are associated with similar attributes as the target contentitem. Examples for identifying published content items associated withsimilar attributes as the target content item are described below.Additional and/or alternative methods for identifying published contentitems associated with similar attributes as the target content item maybe used.

In an embodiment, the content feedback engine determines a number ofcommon attributes between the target content item and each publishedcontent item. The content feedback engine selects the published contentitems associated with the highest numbers of common attributes.Additionally or alternatively, the content feedback engine compares thenumber of common attributes associated with each published content itemwith a minimum threshold. The content feedback engine selects eachpublished content item associated with a number of common attributesabove the minimum threshold.

As an example, a target content item may be a car advertisement to bepublished by Toyota. The target content item may include an image thatshows an aerial view of a yellow car on a curvy road on a cliff. Apublished car advertisement by Audi may include a curvy road and acliff. A published car advertisement by BMW may include a yellow car. Apublished car advertisement by Honda may include an aerial view of acar. Based on the common attributes between the three published caradvertisements and the target content item, a content feedback enginemay determine that the three published car advertisements are associatedwith similar attributes as the target content item.

In an embodiment, the content feedback engine determines characteristicsof a detected audience of each published content item. The contentfeedback engine compares the characteristics of a detected audience of apublished content item to the characteristics of a target audience forthe target content item. The content feedback engine determines a levelof similarity between the characteristics of the detected audience andthe characteristics of the target audience. The content feedback engineselects the published content item based on the level of similarity.

In an embodiment, the content feedback engine determines a score foreach published content item. The content feedback engine compares theattributes associated with the target content item with the attributesassociated with a published content item. The content feedback enginedetermines a level of similarity between the attributes. The contentfeedback engine determines a score for the published content item basedon the level of similarity between the attributes. The content feedbackengine selects the published content items associated with the highestscores. Additionally or alternatively, the content feedback engineselects the published content items associated with scores above aminimum threshold.

As an example, a target content item may be associated with the topics,“Car” and “Gas Efficiency.” A published content item may be associatedwith the topics, “Truck” and “Gas Efficiency.” A content feedback enginedetermines that the topic “Gas Efficiency” is common to both contentitems. The level of similarity between the attributes may be determinedas 1.0. The content feedback engine determines that the topic “Car” isrelated to the topic “Truck.” The level of similarity between theattributes may be determined as 0.8. The content feedback engine maydetermine that the overall level of similarity between the attributes is0.9 (an average of the two individual levels of similarity). Based onthe overall level of similarity, the content feedback engine maydetermine a score for the published content item. The content feedbackengine may determine that the score is above a minimum threshold. Thecontent feedback engine may select the published content item.

In an embodiment, the content feedback engine identifies a subset of thepublished content items that are associated with similar attributes asthe target content item based on a context associated with the targetcontent item. As an example, a target content item may describe a carnamed “Malibu.” However, the word “Malibu” may also refer to aparticular beach in California. A content feedback engine may analyzethe target content item to determine a context of the target contentitem. The content feedback engine may determine that the target contentitem describes cars and driving. A particular set of published contentitems may be associated with the attribute, “Malibu.” The contentfeedback engine may select only a subset of the published content itemsthat are associated with the context of cars and driving.

In an embodiment, the content feedback engine identifies a subset of thepublished content items that include the same word or phrase, image,reference, and/or social handle as a particular portion of the targetcontent item. The content feedback engine identifies the subset ofpublished content items that include both (a) the same word or phrase,image, reference, and/or social handle as a particular portion of thetarget content item and (b) share similar attributes as one or moreother portions of the target content item.

As an example, one published content item may be an advertisement for aprinter. The printer advertisement may state, “Printer A prints at avery high speed.” Another content item may be an advertisement for cars.The car advertisement may state, “Go for speed. Go for Model XYZ.”Meanwhile, a target content item may state, “Our new Printer B printswith exceptional clarity and exceptional speed.”

A content feedback engine may determine that a topic associated with theprinter advertisement is printers, a topic associated with the caradvertisement is cars, and a topic associated with the target contentitem is printers. The content feedback engine may also determine thatthe printer advertisement, the car advertisement, and the target contentitem all include the word “speed.”

The content feedback engine may identify published content itemsassociated similar attributes as the portion, “speed,” of the targetcontent item. Even though both advertisements include the word “speed,”the printer advertisement is associated with the same topic as thetarget content item, while the car advertisement is associated with adifferent topic than the target content item. Hence, the contentfeedback engine may select the printer advertisement but not the caradvertisement.

One or more embodiments include determining effectiveness scores of thepublished content items (Operation 206). As described above withreference to effectiveness scores 120 a-b of FIG. 1, an effectivenessscore may be based on an engagement score and/or a sentiment score.

In an embodiment, the content feedback engine determines an engagementscore of a published content item. The engagement score may bedetermined based on statistics associated with the published contentitem, such as a number of viewers of the published content item, anumber of likes associated with the content item, a number of commentsassociated with the content item, a number of users who shared thecontent item, a number of users who clicked on a hyperlink included inthe content item, and/or a number of users who purchased a productadvertised by the content item.

A webpage or application may concurrently display the published contentitem and the most current statistics for the published content item. Thecontent feedback engine may obtain the most current statistics from thewebpage or application displaying the published content item. As anexample, a top section of a webpage may display a published contentitem. A bottom section of the webpage may display the number of likesreceived for the published content item. Each time a viewer clicks a“Like” button, the number of likes shown on the bottom section may beincremented. A content feedback engine may request to load the webpagedisplaying the published content item. The content feedback engine maydetermine the number of likes received for the published content itemfrom the webpage.

A server hosting the published content item track may track thestatistics associated with the published content item. The contentfeedback engine may transmit a request for the most current statisticsto the server. The content feedback engine may receive a response fromthe server including the most current statistics.

After determining the statistics associated with the published contentitem, the content feedback engine applies a particular function to thestatistics to determine an engagement score. As an example, eachstatistic may be associated with a particular weight. Statistics thatreflect a greater degree of engagement may be associated with a greaterweight. For example, the statistic for the number of users who purchaseda product may be associated with a greater weight. The statistic for thenumber of viewers may be associated with a lesser weight. An engagementscore may be a weighted sum computed based on the statistics.

An engagement score may be determined based on a time period in whichthe engagement with a published content item occurred. As an example, apublished content item may have received 50 comments in the past thirtydays. The published content item may have received 1,000 comments overthirty days ago. A content feedback engine may apply a weight of 0.8 tothe more recent comments. The content feedback engine may apply a weightof 0.2 to the older comments. The content feedback engine may compute aweighted sum as follows, (0.8×50)+(0.2×1,000). The result may be 240.The content feedback engine may determine 240 as the engagement score ofthe published content item.

In an embodiment, the content feedback engine determines a sentimentscore of a published content item. The sentiment score may be determinedbased on whether the comments on the published content item are positiveor negative, and/or whether the published content item is being sharedin a positive or negative manner.

The content feedback engine obtains comments associated with thepublished content item. A webpage or application may concurrentlydisplay the published content item and the comments received for thepublished content item. The content feedback engine may obtain thecomments from the webpage or application displaying the publishedcontent item. As an example, a top section of a webpage may display apublished content item. A bottom section of the webpage may display thecomments received for the published content item. A content feedbackengine may request to load the webpage displaying the published contentitem. The content feedback engine may obtain the comments received forthe published content item from the webpage.

The content feedback engine obtains shares associated with the publishedcontent item. A share associated with a particular published contentitem is another published content item that mentions and/or referencesthe particular published content item. A server hosting the publishedcontent item may monitor the shares associated with the publishedcontent item. The content feedback engine may query the server for theshares associated with the published content item. Additionally oralternatively, the content feedback engine may search through otherpublished content items to find mentions of the published content item.

After obtaining the comments and/or shares associated with the publishedcontent item, the content feedback engine analyzes the comments and/orshares to determine whether the comments and/or shares are positive ornegative. Positive comments and/or shares result in a high sentimentscore. Negative comments and/or shares result in a low sentiment score.The content feedback engine may analyze the comments and/or shares byperforming a natural language analysis and/or a semantic analysis.Additionally or alternatively, the content feedback engine may analyzethe comments and/or shares by detecting certain keywords in the commentsand/or shares.

As an example, a list of keywords may include “hate,” “dislike,” “love,”and “funny.” The words “hate” and “dislike” may be marked as beingnegative. The words “love” and “funny” may be marked as being positive.A content feedback engine may scan through the comments on a publishedcontent item to identify the keywords. The content feedback engine maydetermine the number of positive keywords appearing in the comments. Thecontent feedback engine may determine the number of negative keywordsappearing in the comments. The content feedback engine may determine asentiment score based on the number of positive keywords and the numberof negative keywords appearing in the comments.

In an embodiment, the content feedback engine determines a combinationof an engagement score and a sentiment score. The combination may bebased on an average of the engagement score and the sentiment score.Additionally or alternatively, the combination may be based on otherfunctions applied to the engagement score and the sentiment score.

As an example, a content feedback engine may determine an engagementscore and a sentiment score for a published content item. The contentfeedback engine may compute an average of the engagement score and thesentiment score. The content feedback engine may determine threeeffectiveness scores associated with the published content item: theengagement score, the sentiment score, and the average thereof.

One or more embodiments include determining an effectiveness score forthe portion of the target content item (Operation 208). The contentfeedback engine determines the effectiveness score for the portion ofthe target content item based on the effectiveness scores of thepublished content items determined at Operation 206.

As an example, a content feedback engine may determine the effectivenessscores of published content items associated with similar attributes asa target content item. The content feedback engine may compute anaverage of the effectiveness scores. The content feedback engine maypredict that the effectiveness score for the portion of the targetcontent item is the average of the effectiveness scores of the publishedcontent items.

The effectiveness score for the portion of the target content item maybe determined based on a time period in which the published contentitems were published. A greater weight may be applied to aneffectiveness score of a more recent published content item. A lesserweight may be applied to an effectiveness score of an older publishedcontent item. As an example, a target content item may be a caradvertisement to be published by Toyota. The target content item mayinclude an image of a windy road in a forest. Published content itemswith similar attributes as the target content item and an image of awindy road in a forest may be: a car advertisement by Audi, a caradvertisement by BMW, and a car advertisement by Honda. The Audiadvertisement, the BMW advertisement, and the Honda advertisement mayhave effectiveness scores of 60, 50, and 70, respectively. The Hondaadvertisement may be the oldest published item. The BMW advertisementmay be the next published item. The Honda advertisement may be the mostrecent published item. Based on the chronological ordering of thepublished content items, a content feedback engine may apply theweights, 0.5, 0.3, and 0.2, respectively to the effectiveness scores ofthe Audi advertisement, the BMW advertisement, and the Hondaadvertisement. The content feedback engine may compute a weighted sum asfollows, (0.5×60)+(0.3×50)+(0.2×70). The result may be 59. The contentfeedback engine may determine that the effectiveness score for the imageof the windy road in the forest, in the target content item to bepublished by Toyota, is 59.

One or more embodiments include presenting, at a graphical userinterface, a graphical indication that marks the portion of the targetcontent item based on the effectiveness score for the portion of thetarget content item (Operation 210). The content feedback engine 102determines the graphical indication based on the effectiveness score forthe portion of the target content item. The content feedback engine 102may obtain a set of candidate graphical indications from a datarepository. Each candidate graphical indication corresponds to a rangeof effectiveness scores. Based on the effectiveness score for theportion of the target content item determined at Operation 208, thecontent feedback engine 102 selects one of the candidate graphicalindications.

The content feedback engine 102 displays the graphical indicationconcurrently with the target content item, as the target content item isbeing drafted. The content feedback engine 102 presents a graphicalindication that marks the portion of the target content item based onthe effectiveness score associated with the portion of the targetcontent item, without marking other portions of the target content itembased on the effectiveness score associated with the portion of thetarget content item.

As an example, candidate graphical indications that may be used includea red circle and a green circle. The red circle corresponds toeffectiveness scores greater than or equal to 50. The green circlecorresponds to effectiveness scores less than 50. A content feedbackengine may determine that the word “speed” in a target content item hasan effectiveness score of 59. Based on the effectiveness score of 59,the content feedback engine presents a red circle around the word“speed” in the target content item.

Examples of graphical indications include using different colors,shapes, animations, labels, and/or other marks based on differenteffectiveness scores. As an example, a red circle corresponds toeffectiveness scores between 0 and 30, a blue circle corresponds toeffectiveness scores between 30 and 60, and a green circle correspondsto effectiveness scores between 60 and 100. As an example, changing thecolor of text to red corresponds to effectiveness scores between 0 and50, and changing the color of text to green corresponds to effectivenessscores between 50 and 100. As another example, marking an image with across corresponds to effectiveness scores between 0 and 50, and markingan image with a checkmark corresponds to effectiveness scores between 50and 100. As another example, flashing a portion of a target content itemcorresponds to effectiveness scores between 0 and 30, and displaying theportion without flashing corresponds to effectiveness scores between 30and 100.

In an embodiment, the content feedback engine 102 selects the graphicalindication to be presented based on the effectiveness score for theportion of the target content item as well as other information. As anexample, a content feedback engine may select the graphical indicationto be used based on the effectiveness score for the portion of thetarget content item as well as the identity of the user who is draftingthe target content item. One user may be a famous blogger who has manyfollowers. Another user may be an amateur blogger with fewer followers.The content feedback engine may select different graphical indicationsfor the same portion of the same target content item, based on whetherthe target content item is being drafted by the famous blogger or theamateur blogger.

One or more embodiments include determining whether there are additionalportions of the target content item to analyze (Operation 212). Thecontent feedback engine 102 identifies another portion of the targetcontent item. The content feedback engine 102 may identify portions foranalysis based on semantic analysis, a context of the target contentitem, and/or other information. The content feedback engine 102 maydetermine that common words, such as “a,” “the,” and “since,” are to beexcluded from analysis. Additionally or alternatively, the contentfeedback engine 102 may determine that words that are commonly used in aparticular industry should be analyzed. As an example, a target contentitem may be a car advertisement. A content feedback engine may determinewords that are commonly used in the car industry from a data repository.The words that are commonly include may include: “speed,” “efficiency,”and “power.” The content feedback content may determine that the targetcontent item includes the phrase, “The new Model X is highlygas-efficient.” The content feedback engine may determine that thephrase “gas-efficient” is a portion to be analyzed.

If there is an additional portion to analyze, the content feedbackengine 102 iterates Operations 202-212 with respect to the additionalportion. The content feedback engine 102 may concurrently presentdifferent graphical indications marking different portions of the sametarget content item. As an example, a target content item may includethe phrase, “The new Model X is highly gas-efficient.” A contentfeedback engine may determine that an effectiveness score for“gas-efficient” is 65, while an effectiveness score for “highly” is 30.Based on the different effectiveness scores for the different portions,the content feedback engine may present the phrase “gas-efficient” ingreen text, and the word “highly” in red text.

If there are no additional portions to analyze, one or more embodimentsinclude determining an overall effectiveness score for the targetcontent item (Operation 214). The content feedback engine 102 determinesthe overall effectiveness score for the target content item based oneffectiveness scores corresponding to one or more portions of the targetcontent item.

As an example, a target content item may be a car advertisement,including the text, “The new Model X is highly gas-efficient.” A contentfeedback engine may determine an effectiveness score for the word“gas-efficient.” The content feedback engine may identify published caradvertisements including the word “gas-efficient.” The content feedbackengine may determine engagement scores for each of the published caradvertisements. The content feedback engine may determine a firsteffectiveness score for the word “gas-efficient,” in the target contentitem, based on the engagement scores of the published caradvertisements. Additionally, the content feedback engine may determinesentiment scores for each of the published car advertisements. Thecontent feedback engine may determine a second effectiveness score forthe word “gas-efficient,” in the target content item, based on thesentiment scores of the published car advertisements. The contentfeedback engine may determine an overall effectiveness score, for thetarget content item, based on the first effectiveness score for the word“gas-efficient” and the second effectiveness score for the word“gas-efficient.” The overall effectiveness score may be, for example, anaverage of the first effectiveness score and the second effectivenessscore.

As an example, a target content item may be a car advertisement,including the text, “The new Model X is highly gas-efficient.” A contentfeedback engine may determine an effectiveness score for the word“gas-efficient.” The content feedback engine may identify a first set ofpublished car advertisements including the word “gas-efficient.” Thecontent feedback engine may determine engagement scores for each of thefirst set of published car advertisements. The content feedback enginemay determine a first effectiveness score for the word “gas-efficient,”in the target content item, based on the engagement scores of the firstset of published car advertisements. Additionally, the content feedbackengine may identify a second set of published car advertisementsincluding the word “highly.” The content feedback engine may determineengagement scores for each of the second set of published caradvertisements. The content feedback engine may determine a secondeffectiveness score for the word “highly,” in the target content item,based on the engagement scores of the second set of published caradvertisements. The content feedback engine may determine an overalleffectiveness score based on the first effectiveness score for the word“gas-efficient” and the second effectiveness score for the word“highly.”

The content feedback engine 102 may display the overall effectivenessscore for the target content item as the target content item is beingdrafted. As an example, a user interface for generating and/or modifyinga target content item may be presented. A bottom section of the userinterface may be configured to display a current version of the targetcontent item. The bottom section is also configured to receive userinput specifying and/or modifying the contents of the target contentitem. A top section of the user interface may be configured to displayan overall effectiveness score for the target content item.

As described above with reference to Operation 208, an effectivenessscore for a portion of the target content item is determined. In anembodiment, the content feedback engine 102 presents the overalleffectiveness score for the target content item including the portion ascompared to the overall effectiveness score of the target content itemwithout the portion.

One or more embodiments include determining whether the contents of thetarget content item have been modified (Operation 216). A user maycontinue to modify the target content item as the content feedbackengine 102 performs Operations 202-214. If the user has modified thetarget content item, then the content feedback engine 102 reiteratesOperations 202-214 with respect to the modified target content item.Hence, a content feedback engine 102 may determine effectiveness scoresfor portions of a target content item as the target content item isbeing drafted.

As an example, a user may begin to draft a target content item. Aninitial version of the target content item may state, “Get the bestpizza at Pizza House!” A content feedback engine may perform Operations202-214 based on the initial version of the target content item. Thecontent feedback engine may determine effectiveness scores for portionsof the initial version of the target content item. While Operations202-214 are being performed with respect to the initial version of thetarget content item, the user may add an image of a pizza to the targetcontent item. A second version of the target content item may include:(a) the text “Get the best pizza at Pizza House!” and (b) the pizzaimage. The content feedback engine may reiterate Operations 202-214based on the second version of the target content item. The contentfeedback engine may determine an effectiveness score for portions of thesecond version of the target content item. The user may continue tomodify the target content item. The content feedback engine maydetermine effectiveness scores for portions of the target content itemas the target content item is being modified.

In an embodiment, a content feedback engine 102 performs Operations202-214 on a target content item that has been published. The contentfeedback engine 102 performs Operations 202-214 in order to estimate aneffectiveness score for the target content item and/or a portionthereof. The content feedback engine 102 estimates an effectivenessscore for at least a portion of the target content item based on theeffectiveness scores of other published content items. The effectivenessscores of the other published content items are determined based on, forexample, a level of user engagement and/or user sentiment associatedwith the other published content items. The content feedback engine 102may not necessarily have sufficient monitoring data for determining theeffectiveness score for the target content item based on the level ofuser engagement and/or user sentiment associated with the target contentitem itself.

4. Recommending Content to be Added to and/or Removed from a TargetContent Item

FIG. 3 illustrates an example set of operations for recommending contentto be added to and/or removed from a target content item, in accordancewith one or more embodiments. One or more operations illustrated in FIG.3 may be modified, rearranged, or omitted all together. Accordingly, theparticular sequence of operations illustrated in FIG. 3 should not beconstrued as limiting the scope of one or more embodiments. Theoperations illustrated in FIG. 3 may be performed by a content feedbackengine 102, a content recommendation module 106, another component ormodule, and/or a combination thereof.

One or more embodiments include determining attributes of a targetcontent item (Operation 302). Examples for determining attributes of acontent item are described above with reference to Operation 202. Thetarget content item may be a content item that is being drafted.Additionally or alternatively, the target content item may be a contentitem that has been published.

One or more embodiments include identifying published content itemsassociated with similar attributes as the target content item (Operation304). Examples for identifying published content items associated withsimilar attributes as the target content item are described above withreference to Operation 204.

One or more embodiments include determining effectiveness scores of thepublished content items (Operation 306). Examples for determiningeffectiveness scores of the published content items are described abovewith reference to Operation 206.

One or more embodiments include selecting content from the publishedcontent items based on the respective effectiveness scores (Operation308). A content feedback engine 102 (and/or a content recommendationmodule 106 thereof) selects a subset of the published content itemsbased on the respective effectiveness scores. The content feedbackengine 102 may select published content items that are associated withthe highest effectiveness scores. Additionally or alternatively, thecontent feedback engine 102 may select published content items that areassociated with effectiveness scores that are above a specifiedthreshold.

The content feedback engine 102 compares the selected subset ofpublished content items to identify common content included in theselected subset of published content. The selected subset of publishedcontent may include, for example, a same word, image, social handle,reference, topic, and/or hashtag. The content feedback engine 102selects the common content to be included in a recommendation for thetarget content item. As an example, a content feedback engine maydetermine that each of a selected subset of published content itemsincludes a social handle, “@John_Smith.” The content feedback engine mayselect “@John_Smith” as content to be recommended for a target contentitem. As an example, a content feedback engine may determine that eachof a selected subset of published content items includes a topic,“#new_year.” The content feedback engine may select #new_year” ascontent to be recommended for a target content item.

The content feedback engine 102 compares the selected subset ofpublished content items to identify common attributes associated withthe subset of published content items. The selected subset of publishedcontent may be associated with, for example, a same topic, label,hashtag, and/or geotag. The content feedback engine 102 identifiescontent associated with the common attributes. The content feedbackengine 102 selects the content to be included in a recommendation forthe target content item.

As an example, a content feedback engine may identify a set of publishedcontent items associated with similar attributes as a target contentitem. A subset of the published content items associated with thehighest effectiveness scores may include Published Content Item A andPublished Content Item B. Published Content Item A may state, “The bestplace for a summer vacation is Hawaii.” Published Content Item B maystate, “Tahoe is ski heaven.” The two published content items do notshare any common content. However, both published content items may beassociated with a common attribute, the topic “Vacation.” The contentfeedback engine may identify content associated with the topic“Vacation,” such as the text “memorable vacation,” and an image of arelaxed person. The content feedback engine may recommend adding thecontent to the target content item.

The content feedback engine 102 selects a subset of published contentitems that are associated with relatively low effectiveness scores. Thecontent feedback engine 102 identifies common content and/or attributesassociated with the subset of published content items with loweffectiveness scores. The content feedback engine 102 recommendsremoving, from the target content item, the content and/or attributesthat are common to the published content items with low effectivenessscores.

As an example, a target content item may state, “Company MedDevice isintroducing a new medical stent.” A published content item with similarattributes as the target content item may state, “A new pacemaker,introduced by Company HeartCare, is available on the market.” Anotherpublished content item with similar attributes as the target contentitem may state, “With the exciting launch of the new medical machine,Company HealthMachine is making significant advancements in improvingpatient health.” A content feedback engine may determine that aneffectiveness score of the Company HeartCare content item is low, and aneffectiveness score of the Company HealthMachine content item is high.The content feedback engine may determine that the word “introduce” isassociated with a low effectiveness score. The content feedback enginemay determine that the words “launch” and “exciting” are associated witha high effectiveness score. The content feedback engine may recommendadding the text “launch” and “exciting” to the target content item. Thecontent feedback engine may recommend replacing the text, “CompanyMedDevice is introducing a new medical stent” with “Company MedDevice islaunching a exciting new medical stent.”

One or more embodiments include recommending addition and/or removal ofthe selected content to the target content item (Operation 310). Thecontent feedback engine 102 generates a recommendation for adding theselected content to the target content item. Additionally oralternatively, the content feedback engine 102 generates arecommendation for removing the selected content from the target contentitem. Additionally or alternatively, the content feedback engine 102generates a recommendation for replacing current content, in the targetcontent item, with content selected from published content items.

The content feedback engine 102 may cause the recommendation to bedisplayed to the side of the target content item. The recommendation mayinclude an arrow pointing to a particular location in the target contentitem at which the content should be added. As an example, a userinterface for generating and/or modifying a target content item may bepresented. A central section of the user interface may be configured toreceive user input specifying the contents of the target content item. Aside section of the user interface may be configured to display one ormore recommendations.

The content feedback engine 102 may cause the recommendation to bedisplayed at a particular location within the target content item. Thecontent feedback engine 102 displays a user interface element forreceiving user input accepting or rejecting the recommended content.Additionally or alternatively, the content feedback engine 102 acceptsuser input moving the recommended content to a different location withinthe target content item.

One or more embodiments include determining an overall effectivenessscore for the target content item with the recommended content(Operation 312). The content feedback engine 102 determines an overalleffectiveness score for the target content item as if the recommendedcontent were included in the target content item. Examples fordetermining an overall effectiveness score for a target content item aredescribed above with reference to Operations 202-214 of FIG. 2.

The content feedback engine 102 may analyze the overall effectivenessscore to determine whether to recommend different content for additionto the target content item.

As an example, the content feedback engine 102 may compare the overalleffectiveness score to a minimum threshold. If the minimum threshold isnot satisfied, then the content feedback engine 102 may reiterateOperations 308-310 to recommend different content for addition to thetarget content item.

As another example, the content feedback engine 102 may compare (a) theoverall effectiveness score of the target content item with therecommended content and (b) an overall effectiveness score of the targetcontent item without the recommended content. If there is no improvementin the overall effectiveness score based on the recommended content,then the content feedback engine 102 may reiterate Operations 308-310 torecommend different content for addition to the target content item.Hence, the content feedback engine 102 recommends content for inclusioninto the target content item in order to increase the overalleffectiveness score for the target content item.

One or more embodiments include determining whether the contents of thetarget content item have been modified (Operation 314). A user maymodify the target content item by accepting or rejecting the recommendedcontent. Additionally or alternatively, the user may make othermodifications to the target content item. The user may continue tomodify the target content item as the content feedback engine 102performs Operations 302-312. If the user has modified the target contentitem, then the content feedback engine 102 reiterates Operations 302-312with respect to the modified target content item. Hence, a contentfeedback engine 102 may recommend content for a target content item asthe target content item is being drafted.

One or more embodiments include determining whether the target contentitem has been published (Operation 316). The content feedback engine 102receives user input indicating publication of the target content item.The target content item may be published to one or more public forums.

One or more embodiments include comparing (a) an actual effectivenessscore for the target content item that was published and (b) thepredicted effectiveness score for the target content item with therecommended content (Operation 318).

The content feedback engine 102 determines an actual effectiveness scorefor the target content item that was published. Examples for determiningan effectiveness score of a published content item are described abovewith reference to Operation 206.

The content feedback engine 102 compares the effectiveness score for thetarget content item that was published and the overall effectivenessscore predicted at Operation 312. As an example, the content feedbackengine may determine whether the actual effectiveness score is higherthan the predicted effectiveness score. As another example, the contentfeedback engine may determine whether a difference between the actualeffectiveness score and the predicted effectiveness score is above aspecified threshold.

The content feedback engine 102 performs one or more actions based onthe comparison between the actual effectiveness score and the predictedeffectiveness score. As an example, a dashboard may display a list ofcontent items drafted by a particular user. A content feedback enginemay cause the effectiveness score and/or the predicted effectivenessscore to be displayed on the dashboard. As another example, a contentfeedback engine may transmit a message to the user who drafted thetarget content item. The message may inform the user of theeffectiveness score and/or the predicted effectiveness score.

5. Example Embodiments

Detailed examples are described below for purposes of clarity.Components and/or operations described below should be understood asspecific examples which may not be applicable to certain embodiments.Accordingly, components and/or operations described below should not beconstrued as limiting the scope of any of the claims.

FIG. 4 illustrates an example user interface for presenting a predictedeffectiveness score and recommended content for a target content item,in accordance with one or more embodiments.

As illustrated, FIG. 4 includes a user interface 402 for generatingand/or modifying a target content item 404. The target content item 404is displayed in the bottom-center of the user interface 402. The targetcontent item 404 may include the following content: a logo 422, a headerimage 424, text 426, social handles 428, references 430, and images 431.

The target content item may be an advertisement to be published byCompany Matrix. A user types enters the following text into the targetcontent item 404, “Today, Company Matrix announced the launch of itsnewest software product: Software Illusion.”

A content feedback engine determines attributes of an initial version ofthe target content item 404, which states, “Today, Company Matrixannounced the launch of its newest software product: Software Illusion.”The attributes associated with the target content item 404 include,“Software” and “Company Matrix.” Further, the content feedback enginedetermines that “Cloud Computing” is associated with “Company Matrix.”The content feedback engine additionally identifies “Cloud Computing” asan attribute associated with the target content item 404.

The content feedback engine identifies published content items withsimilar attributes as the target content item 404. The content feedbackengine determines effectiveness scores of the published content items.

As the content feedback engine identifies the published content itemsand determines the respective effectiveness scores, the user modifiesthe target content item 404 via the user interface 402. The user typesin the following text, “Software Illusion includes the followingfeatures: high speed, high resiliency, and high scalability.” Therefore,a second version of the target content item 404 states, “Today, CompanyMatrix announced the launch of its newest software product: SoftwareIllusion. Software Illusion includes the following features: high speed,high resiliency, and high scalability.”

Meanwhile, the content feedback engine identifies the followingpublished content items associated with similar attributes as theinitial version of the target content item:

(a) A news article states, “The stock prices of Company A are rising inanticipation of its flagship cloud software, Software XYZ.” Anengagement score of the news article is 60. A sentiment score of thenews article is 65.

(b) An advertisement by Company Nebnet states, “What we need for cloudcomputing is reliability.” An engagement score of the Nebnetadvertisement is 80. A sentiment score of the Nebnet adverstisement is75.

(c) a blog post by a commentator John Smith states, “There are too manysoftware products nowadays.” An engagement score of the blog post is 30.A sentiment score of the blog post is 20.

The content feedback engine predicts an engagement score, a sentimentscore, and a combined score for the initial version of the targetcontent item 404. The predicted engagement score 412 is an average ofthe engagement scores of the news article, the Nebnet advertisement, andthe blog post, which is 50. The predicted sentiment score 414 is anaverage of the sentiment scores of the news article, the Nebnetadvertisement, and the blog post, which is 45. The predicted combinedscore 416 is an average of the predicted engagement score 412 and thepredicted sentiment score 414, which is 47.5. The content feedbackengine displays the predicted engagement score 412, the predictedsentiment score 414, and the predicted combined score 416 on the userinterface 402. The predicted engagement score 412, the predictedsentiment score 414, and the predicted combined score 416 are displayedas sliding bars above the target content item 404.

The content feedback engine selects content from the published contentitems for recommending to the initial version of the target content item404. The content feedback engine determines that the effectivenessscores of the news article and the Nebnet advertisement are above aspecified threshold of 50. The content feedback engine identifies commoncontent and/or attributes associated with the news article and theNebnet advertisement. A common attribute may be, “Cloud Computing.” Thecontent feedback engine determines that the following content isassociated with the attribute “Cloud Computing”: the text “cloudcomputing,” an image of servers, the social handle “@GadgetGuy,” and thereference “An Article on Cloud Networking.”

The content feedback engine displays the text “cloud computing” asrecommended text 436. The content feedback engine displays the image ofservers as a recommended image 441. The content feedback engine displaysthe social handle “@GadgetGuy” as a recommended social handle 438. Thecontent feedback engine displays the reference “An Article on CloudNetworking” as a recommended reference 440. The recommended text 436,recommended social handle 438, recommended reference 440, andrecommended image 441 are displayed on a side section of the userinterface 402, adjacent to the target content item 404.

The content feedback engine receives user input accepting allrecommendations. The content feedback engine determines that the contentof the target content item 404 has been modified. A third version of thetarget content item 404 now states, “Today, Company Matrix announced thelaunch of its newest cloud computing software product: SoftwareIllusion. Software Illusion includes the following features: high speed,high resiliency, and high scalability.” The third version of the targetcontent item 404 also includes the image of servers, the social handle“@GadgetGuy,” and a reference to “An Article on Cloud Networking.”

The content feedback engine determines attributes of the third versionof the target content item 404. The content feedback engine identifiespublished content items with similar attributes as the target contentitem 404. The content feedback engine determines effectiveness scores ofthe published content items.

The content feedback engine predicts a new engagement score, sentimentscore, and combined score for the third version of the target contentitem 404 based on the effectiveness scores of the published contentitems. The new predicted engagement score 412 is 65. The new predictedsentiment score 414 is 55. The new predicted combined score 416 is 60.The content feedback engine adjusts the sliding bars above the targetcontent item 404 to reflect the new predicted scores.

The content feedback engine selects content from the published contentitems for recommending to the third version of the target content item404. The content feedback engine selects a particular logo and aparticular header image for recommending to the target content item 404.The content feedback engine displays the particular logo as recommendedlogo 432. The content feedback engine displays the particular headerimage as recommended image 434.

The content feedback engine receives user input rejecting therecommendations. Subsequently, the user selects the “Publish” button452. The third version of the target content item 404 is published to asocial media platform.

FIG. 5 illustrates an example user interface for presenting graphicalindications that mark portions of a target content item based oneffectiveness scores for the portions of the target content item, inaccordance with one or more embodiments.

As illustrated, FIG. 5 includes a user interface 502 for generatingand/or modifying a target content item 504. The target content item 504is displayed in the bottom-center of the user interface 502. The targetcontent item 504 includes the text “Our cloud computing product,Software Illusion, includes the following features: high speed and highscalability. @Cloud_Expert endorses us!” Within the text, @Cloud_Expertis a social handle referring to a particular user of a social mediaplatform. The target content item 504 also includes an image 524 of acloud server. The target content item 504 is an advertisement for acloud computing software product.

A content feedback engine identifies portions 532-538 for analysis.Portion 532 includes “high speed.” Portion 534 includes “highscalability.” Portion 536 includes “@Cloud_Expert.” Portion 538 includesthe image 524.

The content feedback engine identifies a set of published content itemssharing similar attributes as the target content item 504. The set ofpublished content items are all advertisements for cloud computingsoftware products. The set of published content items include a total of100 published content items. A first subset of the 100 published contentitems includes the phrase “high speed.” A second subset of the 100published content items includes the phrase “high scalability.” A thirdsubset of the 100 published content items includes the social handle“@Cloud_Expert.” A fourth subset of the 100 published content itemsincludes an image of a cloud server similar to the image 524.

The content feedback engine determines effectiveness scores of the firstsubset of published content items (which include the phrase “highspeed”). The content feedback engine determines that an average of theeffectiveness scores of the first subset of published content items is40. The content feedback engine determines that an effectiveness scoreof 40 corresponds to the portion 532 of the target content item 504.

The content feedback engine determines effectiveness scores of thesecond subset of published content items (which include the phrase “highscalability”). The content feedback engine determines that an average ofthe effectiveness scores of the second subset of published content itemsis 80. The content feedback engine determines that an effectivenessscore of 80 corresponds to the portion 534 of the target content item504.

The content feedback engine determines effectiveness scores of the thirdsubset of published content items (which include the social handle“@Cloud_Expert”). The content feedback engine determines that an averageof the effectiveness scores of the third subset of published contentitems is 30. The content feedback engine determines that aneffectiveness score of 30 corresponds to the portion 536 of the targetcontent item 504.

The content feedback engine determines effectiveness scores of thefourth subset of published content items (which include an image of acloud server similar to the image 524). The content feedback enginedetermines that an average of the effectiveness scores of the fourthsubset of published content items is 70. The content feedback enginedetermines that an effectiveness score of 70 corresponds to the portion538 of the target content item 504.

The content feedback engine determines that there are two candidategraphical indications that may be presented. A solid-line circle may bepresented based on an effectiveness score for a portion of a targetcontent item that is between 0 and 50. A dotted-line circle may bepresented based on an effectiveness score for a portion of a targetcontent item that is between 50 and 100.

Based on the effectiveness scores of each portion 532-538, the contentfeedback engine presents a graphical indication 542-548 that marks eachportion 532-538. Specifically, since the effectiveness score of portion532 is determined as 40, the content feedback engine presents asolid-line circle around portion 532. Since the effectiveness score ofportion 534 is determined as 80, the content feedback engine presents adotted-line circle around portion 534. since the effectiveness score ofportion 536 is determined as 30, the content feedback engine presents asolid-line circle around portion 536. Since the effectiveness score ofportion 538 is determined as 70, the content feedback engine presents adotted-line circle around portion 538.

The content feedback engine determines an overall engagement score 512for the target content item 504 based on the engagement scores of theset of published content items sharing similar attributes as the targetcontent item 504.

The content feedback engine determines an overall sentiment score 514for the target content item 504 based on the sentiment scores of the setof published content items sharing similar attributes as the targetcontent item 504.

The content feedback engine determines an overall combined score 516 forthe target content item 504 based on the overall engagement score 512and the overall sentiment score 514. Additionally or alternatively, thecontent feedback engine determines an overall combined score 516 for thetarget content item 504 based on engagement scores and/or sentimentscores associated with the first subset of published content items, thesecond subset of published content items, the third subset of publishedcontent items, and the fourth subset of published content items.

6. Miscellaneous; Extensions

Embodiments are directed to a system with one or more devices thatinclude a hardware processor and that are configured to perform any ofthe operations described herein and/or recited in any of the claimsbelow.

In an embodiment, a non-transitory computer readable storage mediumcomprises instructions which, when executed by one or more hardwareprocessors, causes performance of any of the operations described hereinand/or recited in any of the claims.

Any combination of the features and functionalities described herein maybe used in accordance with one or more embodiments. In the foregoingspecification, embodiments have been described with reference tonumerous specific details that may vary from implementation toimplementation. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the invention, and what isintended by the applicants to be the scope of the invention, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

7. Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), or network processing units (NPUs)that are persistently programmed to perform the techniques, or mayinclude one or more general purpose hardware processors programmed toperform the techniques pursuant to program instructions in firmware,memory, other storage, or a combination. Such special-purpose computingdevices may also combine custom hard-wired logic, ASICs, FPGAs, or NPUswith custom programming to accomplish the techniques. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques.

For example, FIG. 6 is a block diagram that illustrates a computersystem 600 upon which an embodiment of the invention may be implemented.Computer system 600 includes a bus 602 or other communication mechanismfor communicating information, and a hardware processor 604 coupled withbus 602 for processing information. Hardware processor 604 may be, forexample, a general purpose microprocessor.

Computer system 600 also includes a main memory 606, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 602for storing information and instructions to be executed by processor604. Main memory 606 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 604. Such instructions, when stored innon-transitory storage media accessible to processor 604, rendercomputer system 600 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 600 further includes a read only memory (ROM) 608 orother static storage device coupled to bus 602 for storing staticinformation and instructions for processor 604. A storage device 610,such as a magnetic disk or optical disk, is provided and coupled to bus602 for storing information and instructions.

Computer system 600 may be coupled via bus 602 to a display 612, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 614, including alphanumeric and other keys, is coupledto bus 602 for communicating information and command selections toprocessor 604. Another type of user input device is cursor control 616,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 604 and forcontrolling cursor movement on display 612. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 600 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 600 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 600 in response to processor 604 executing one or more sequencesof one or more instructions contained in main memory 606. Suchinstructions may be read into main memory 606 from another storagemedium, such as storage device 610. Execution of the sequences ofinstructions contained in main memory 606 causes processor 604 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 610.Volatile media includes dynamic memory, such as main memory 606. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge,content-addressable memory (CAM), and ternary content-addressable memory(TCAM).

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 602. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 604 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 600 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 602. Bus 602 carries the data tomain memory 606, from which processor 604 retrieves and executes theinstructions. The instructions received by main memory 606 mayoptionally be stored on storage device 610 either before or afterexecution by processor 604.

Computer system 600 also includes a communication interface 618 coupledto bus 602. Communication interface 618 provides a two-way datacommunication coupling to a network link 620 that is connected to alocal network 622. For example, communication interface 618 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 618 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 618sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 620 typically provides data communication through one ormore networks to other data devices. For example, network link 620 mayprovide a connection through local network 622 to a host computer 624 orto data equipment operated by an Internet Service Provider (ISP) 626.ISP 626 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 628. Local network 622 and Internet 628 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 620and through communication interface 618, which carry the digital data toand from computer system 600, are example forms of transmission media.

Computer system 600 can send messages and receive data, includingprogram code, through the network(s), network link 620 and communicationinterface 618. In the Internet example, a server 630 might transmit arequested code for an application program through Internet 628, ISP 626,local network 622 and communication interface 618.

The received code may be executed by processor 604 as it is received,and/or stored in storage device 610, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A non-transitory computer readable mediumcomprising instructions which, when executed by one or more hardwareprocessors, cause performance of operations comprising: determining afirst effectiveness score corresponding to a first subset of publishedcontent items that share one or more attributes with at least a firstportion of a target content item that is being drafted; wherein thefirst effectiveness score predicts how effective a hypothetical contentitem having the first portion would be as a social post accessible toother users on at least one social networking platform; and presenting,on a graphical user interface for drafting the target content item, afirst graphical indication that marks the first portion of the targetcontent item based on the first effectiveness score, without marking oneor more other portions of the target content item based on the firsteffectiveness score.
 2. The medium of claim 1, wherein the operationsfurther comprise: determining a second effectiveness score correspondingto a second subset of published content items that share one or moreattributes with at least a second portion of the target content itemthat is being drafted; and presenting, on the graphical user interfacefor drafting the target content item, a second graphical indication thatmarks the second portion of the target content item based on the secondeffectiveness score.
 3. The medium of claim 1, wherein the operationsfurther comprise: identifying a second subset of published content itemsthat share one or more attributes with at least the first portion of thetarget content item that is being drafted; selecting a portion of thesecond subset of published content items; and generating arecommendation to add the selected portion of the second subset ofpublished content items into the target content item.
 4. The medium ofclaim 3, wherein the selected portion of the second subset of publishedcontent items comprises a social handle.
 5. The medium of claim 3,wherein the selected portion of the second subset of published contentitems comprises a topic.
 6. The medium of claim 1, wherein theoperations further comprise: identifying a second subset of publishedcontent items that share one or more attributes with at least the firstportion of the target content item that is being drafted; selecting aportion of the second subset of published content items; and generatinga recommendation to replace current content in the target content itemwith the selected portion of the second subset of published contentitems.
 7. The medium of claim 1, wherein the operations furthercomprise: determining an overall effectiveness score of the targetcontent item based on the first effectiveness score and a secondeffectiveness score, wherein the second effectiveness score correspondsto any portion of the target content item; and presenting, on thegraphical user interface for drafting the target content item, a secondgraphical indication based on the overall effectiveness score, a thirdgraphical indication based on the first effectiveness score, and afourth graphical indication based on the second effectiveness score. 8.The medium of claim 7, wherein the first effectiveness score is based onan engagement score corresponding to each of the first subset ofpublished content items, and the second effectiveness score is based ona sentiment score corresponding to each of the first subset of publishedcontent items.
 9. The medium of claim 8, wherein the operations furthercomprise: determining the engagement score corresponding to a particularpublished content item, of the first subset of published content items,based on at least one of: (a) a number of users that viewed theparticular published content item, and (b) a number of users that tookan action in association with the particular published content item. 10.The medium of claim 8, wherein the operations further comprise:determining the sentiment score corresponding to a particular publishedcontent item, of the first subset of published content items, based on alevel of positivity or negativity corresponding to actions taken inassociation with the particular published content item.
 11. A method,comprising: determining a first effectiveness score corresponding to afirst subset of published content items that share one or moreattributes with at least a first portion of a target content item thatis being drafted; wherein the first effectiveness score predicts howeffective a hypothetical content item having the first portion would beas a social post accessible to other users on at least one socialnetworking platform; and presenting, on a graphical user interface fordrafting the target content item, a first graphical indication thatmarks the first portion of the target content item based on the firsteffectiveness score, without marking one or more other portions of thetarget content item based on the first effectiveness score, wherein themethod is performed by at least one device including a hardwareprocessor.
 12. The method of claim 11, further comprising: determining asecond effectiveness score corresponding to a second subset of publishedcontent items that share one or more attributes with at least a secondportion of the target content item that is being drafted; andpresenting, on the graphical user interface for drafting the targetcontent item, a second graphical indication that marks the secondportion of the target content item based on the second effectivenessscore.
 13. The method of claim 11, further comprising: identifying asecond subset of published content items that share one or moreattributes with at least the first portion of the target content itemthat is being drafted; selecting a portion of the second subset ofpublished content items; and generating a recommendation to add theselected portion of the second subset of published content items intothe target content item.
 14. The method of claim 13, wherein theselected portion of the second subset of published content itemscomprises a social handle.
 15. The method of claim 13, wherein theselected portion of the second subset of published content itemscomprises a topic.
 16. The method of claim 11, further comprising:identifying a second subset of published content items that share one ormore attributes with at least the first portion of the target contentitem that is being drafted; selecting a portion of the second subset ofpublished content items; and generating a recommendation to replacecurrent content in the target content item with the selected portion ofthe second subset of published content items.
 17. A system comprising:at least one device including a hardware processor; and the system beingconfigured to perform operations comprising: determining a firsteffectiveness score corresponding to a first subset of published contentitems that share one or more attributes with at least a first portion ofa target content item that is being drafted; wherein the firsteffectiveness score predicts how effective a hypothetical content itemhaving the first portion would be as a social post accessible to otherusers on at least one social networking platform; and presenting, on agraphical user interface for drafting the target content item, a firstgraphical indication that marks the first portion of the target contentitem based on the first effectiveness score, without marking one or moreother portions of the target content item based on the firsteffectiveness score.
 18. The system of claim 17, wherein the operationsfurther comprise: determining a second effectiveness score correspondingto a second subset of published content items that share one or moreattributes with at least a second portion of the target content itemthat is being drafted; and presenting, on the graphical user interfacefor drafting the target content item, a second graphical indication thatmarks the second portion of the target content item based on the secondeffectiveness score.
 19. The system of claim 17, wherein the operationsfurther comprise: identifying a second subset of published content itemsthat share one or more attributes with at least the first portion of thetarget content item that is being drafted; selecting a portion of thesecond subset of published content items; and generating arecommendation to add the selected portion of the second subset ofpublished content items into the target content item.
 20. The system ofclaim 17, wherein the operations further comprise: identifying a secondsubset of published content items that share one or more attributes withat least the first portion of the target content item that is beingdrafted; selecting a portion of the second subset of published contentitems; and generating a recommendation to replace current content in thetarget content item with the selected portion of the second subset ofpublished content items.